little-book-rl

A concise introduction to reinforcement learning, from fundamentals to applied algorithms. Use the book to learn the subject and its PyTorch examples and supplementary proofs to study key methods in more detail.

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Overview

The Little Book of Reinforcement Learning is a short introduction to reinforcement learning, covering basics through applied algorithms. It is for readers who want a compact way to study the subject and see how its methods are implemented. The repository includes the book, PyTorch implementations of algorithms covered in it, ranging from Monte Carlo to PPO, and supplementary explanations and rigorous proofs for dynamic programming algorithms that the book covers briefly.

Key features

  • Short introduction to reinforcement learning fundamentals and applied algorithms
  • PyTorch implementations of algorithms from Monte Carlo to PPO
  • Supplementary explanations and rigorous proofs for dynamic programming algorithms

Best for

Readers looking for a concise introduction to reinforcement learning with code examples. The supplementary material is useful when they want a more detailed treatment of dynamic programming.

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alxndrTL/little-book-rl
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Guo-astro/little-book-rl
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Learning, interviews and curated lists
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No license declaredWithout a license, the author keeps all rights. Ask the upstream owner before reusing the code.
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2026-10-14
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In syncLast synced 2026-11-08

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